The Harvard Business Review has labelled data science the "sexiest job of the 21st century".

If they meant that jobs in data science are increasing dramatically, that data scientists can work in fields as diverse as health, retail or ecology, and that data scientists are commanding high salaries, then they were spot on.

After all, we're creating more than 2.5 exabytes of data every day. Someone needs to make sense of it all.

A course tailored to you

You'll enter the Master of Data Science with a background in computer science or statistics (or both), and the course will be tailored to build your skills in the other discipline.

Flexibility to follow your interests

Core subjects will give you a solid grounding in data science, so you’ll have the technological and analytical abilities that are vital for managing and interpreting large, complex collections of data.

Beyond the core subjects, elective subjects give you the freedom to dive deeper into a specialist area of data science.

Sharpen your skills with the capstone project

You’ll leave the course with a major data science project to feature in your CV. In the capstone project, you’ll apply data science tools to a practical problem, working individually or as part of a team to showcase your skills.

More than just technical skills

We know that you’ll need more than technical capability to succeed in the workplace. To round out your skill set, you can choose from professional skills subjects, such as scientific communication, so you can start your data science career with confidence.

If you’d like to gain more real-world experience, you can choose to complete an internship in a science or technology-related workplace for course credit.

The Master of Data Science is a 200-point program, made up of:

• 3 Core statistics subjects (37.5 points)
• 3 Core computer science subjects (37.5 points)
• Elective subjects (50 points), including prerequisite subjects if needed (up to 50 points), data science or professional skills subjects or a research project.
• Capstone data science project (25 points)

Your elective subjects will be tailored to you, depending on your previous academic background and your interests.

First, you’ll need to look at whether you need any prerequisite subjects:

• If you have a statistics background, you’ll complete computer science prerequisite subjects.
• If you have a computer science background, you’ll complete statistics prerequisite subjects.
• If you meet both the computer science and statistics prerequisites, no prerequisite subjects are needed.

If you are coming from the Graduate Diploma in Data Science or a University of Melbourne Data Science undergraduate major (or equivalent), you may be eligible for an accelerated 1.5-year (150-point) program, receiving up to 50 points of credit.

Once you’ve taken care of any prerequisites, you can choose from a diverse list of data science or professional skills electives. If you’d like to gain experience in a science and technology workplace or with research, you can do an 80–100-hour internship subject or take on an additional research project.

All students undertake a data science capstone project, over one academic year, working on a practical data science research question either individually or as part of a team.

Career outcomes

Our graduates go on to work as data scientists and analysts, software engineers, data infrastructure engineers, business intelligence analysts and statisticians.

Employers in this field include:

• Consulting firms such as EY, KPMG and Accenture
• Financial services companies including Citibank, ANZ, CBA and NAB
• IT and telecommunication companies such as IBM, Microsoft and Telstra
• Government departments and organisations, such as the Australian Bureau of Statistics
• Universities and public research institutions such as the CSIRO.

Technical and professional skills

On graduating from the course you’ll have a sound knowledge of modern statistical methodology and computing that will equip you for a career in data science and enable your career to develop as data science evolves.

Graduates will:

• Have the ability to demonstrate advanced independent critical enquiry, analysis and reflection
• Have a strong sense of intellectual integrity and the ethics of scholarship
• Have in-depth knowledge of modern statistical methodology and computing
• Reach a high level of achievement in writing, research or project activities, problem-solving and communication
• Be critical and creative thinkers, with an aptitude for continued self-directed learning
• Be able to examine critically, synthesise and evaluate knowledge across a broad range of disciplines
• Have a set of flexible and transferable skills for different types of employment
• Be able to initiate and implement constructive change in their communities, including professions and workplaces.

Further study

At the end of the course, if you complete the optional research project, you could qualify to undertake a PhD (Doctor of Philosophy).

To be considered for entry, you must have completed: An undergraduate degree with a major in a relevant discipline (computer science, data science or statistics) with a weighted average mark

IELTS (academic English Only): 6.5 (no band less than 6.0)
TOEFL Internet-based test: 79 + ; Writing 21; Speaking 18; Reading 13; Listening 13;
Pearson Test of English Academic: 58 + no communicative skill below 50
Cambridge English: Advanced/ Certificate of Advanced English (CAE): 176 + no skill below 169.